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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
1278b51918b453a40540bc95a61aa59390998f95 | 3d93a8b31c1ca565df5a8dfd0079a8892da44e63 | /1M_project.R | 2bce348c349619b7ecba0d541c102e34e8d310e7 | [] | no_license | KFc4dinnah/1M_Project | bf79dac596414eb92765f73109a270eb74e7ddec | e30c088141864a7ef0f51aa7ba9ab991a40bb44a | refs/heads/main | 2023-08-23T02:39:32.792274 | 2021-09-29T07:27:04 | 2021-09-29T07:27:04 | 411,541,272 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,384 | r | 1M_project.R | library(dplyr)
library(tidyverse)
library(readxl)
library(lubridate)
library(plyr)
library(arules)
library(arulesViz)
#import the data
df <- read_excel("C:/Users/khiem.phung/Downloads/Test_Data_Skill.xlsx",
sheet = "Data")
###SQL test
#first two services and the date
df_fir... |
cb26ea00b68d5fcf56f94716b35e7d0b6d1a66d9 | 0844c816ade1b06645fd9b3ae661c0691978117b | /prep/example_json.R | bbb9c2eef17d94e812199a18536ea6b446aeeb3c | [
"MIT"
] | permissive | petrbouchal/pragr | d92966395ac79982c72ba3a14fae6203176b256e | 858fbb96f69eda9ac0922e8dfbc966948e545073 | refs/heads/master | 2022-12-21T20:44:41.420783 | 2022-12-17T21:18:35 | 2022-12-17T21:18:35 | 186,895,587 | 3 | 0 | null | null | null | null | UTF-8 | R | false | false | 252 | r | example_json.R | library(sf)
st_read("https://ags.arcdata.cz/arcgis/rest/services/OpenData/AdministrativniCleneni_v12/MapServer/10/query?where=KOD_OBEC%20like%20%27%25554782%25%27&returnGeometry=true&outFields=*&f=json&&resultRecordCount=200") %>%
plot(max.plot = 1)
|
b7fb994e6a63d7576dc64cc9d324ef1453bb7f2a | 9ec64d2b674f77c8c61e9023f08f286a13ecc2e0 | /Exploratory-Data/course-project/plot6.R | 0a56048cc4652f358047a3cf4e1eeb11e7ff810f | [] | no_license | anabaraldi/data-science | 677fafc52378b6c3d1656162ee090e0b8db13398 | 8ddbfc364a87472e3a0054b2ed5bf0ab5a984eb5 | refs/heads/master | 2020-04-06T07:01:12.833087 | 2016-09-04T23:17:26 | 2016-09-04T23:17:26 | 30,598,445 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,247 | r | plot6.R | #libraries
library(dplyr)
library(ggplot2)
# Reading and cleaning data
unzip("exdata-data-NEI_data.zip")
# This first line will likely take a few seconds. Be patient!
NEI <- readRDS("summarySCC_PM25.rds")
SCC <- readRDS("Source_Classification_Code.rds")
# The following code will filter the SCC file so we can subset ... |
47583f0fe210a33fe0ebd0a399a18fa65ff0706a | b77b91dd5ee0f13a73c6225fabc7e588b953842b | /shared_functions/point_to_nearest_coastline.R | 0c2623b79cde3061be1e3f7dff00d74f7546e307 | [
"MIT"
] | permissive | ksamuk/gene_flow_linkage | a1264979e28b61f09808f864d5fa6c75568147b0 | 6182c3d591a362407e624b3ba87403a307315f2d | refs/heads/master | 2021-01-18T09:18:02.904770 | 2017-04-02T16:51:40 | 2017-04-02T16:51:40 | 47,041,898 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 791 | r | point_to_nearest_coastline.R | # move a point to the nearest coastline
point_to_nearest_coastline <- function (bat, loc, mode){
nearest.coastline <- NA
dist.to.coast1 <- NA
dist.to.coast2 <- NA
try(nearest.coastline <- dist2isobath(bat, loc, isobath = -10))
if (!is.na(nearest.coastline[,1][1])){
loc$x <- nearest.coastline[,4]
loc$y ... |
22c10ecb604878563720a99410e2961ace122fe9 | efeba9f5aff2e7afbf96a57e0baf62a8fb1a3b94 | /Part2/Stage3-Structured data/SeoulClinic analysis.R | 242931eb4abf072862c168ea23d5194ac86e9d43 | [] | no_license | psm9619/R_Data_Analysis | b1db04295607b5b0811eb2151ce5378a812b2aa3 | b6b8186a582174533ab41a68aeab77bdcf0ea854 | refs/heads/master | 2020-05-29T13:27:26.350660 | 2019-10-10T01:07:53 | 2019-10-10T01:07:53 | 189,161,472 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 844 | r | SeoulClinic analysis.R |
library(ggplot2)
data1 <- read.csv("2013๋
_์์ธ_์ฃผ์๊ตฌ๋ณ_๋ณ์ํํฉ.csv")
data1
barplot(as.matrix(data1[1:9,2:11]),
main=paste("์์ธ์ ์ฃผ์๊ตฌ๋ณ ๊ณผ๋ชฉ๋ณ ๋ณ์ํํฉ-2013๋
", "\n", "์ถ์ฒ "),
ylab = "๋ณ์์", beside = T, col= rainbow(8))
abline (h=seq(0,350,10), lty=3, lwd=0.2)
name <- data1$ํ์๊ณผ๋ชฉ
## to draw in ggplot, you can't use wide ta... |
21f8c962c1509f7e35b917d859f06948b3a68277 | 74d8df7e5a0fd61394fd0494f35ce82dfaa30c96 | /man/generateFreqs.Rd | e8d22e196f31d01f22c9490f29528148a38f77ca | [
"MIT"
] | permissive | immunogenomics/scpost | 8bde0fff6be217aa92e5b2cb48d145cd35031343 | 9e6ce336addc7e0d50e266299e8b46bed7df78d0 | refs/heads/main | 2023-04-13T13:15:08.708526 | 2021-07-22T14:14:36 | 2021-07-22T14:14:36 | 312,683,900 | 3 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,230 | rd | generateFreqs.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/generateFrequencies.R
\name{generateFreqs}
\alias{generateFreqs}
\title{Generate cell state frequency distributions for samples}
\usage{
generateFreqs(
batchStructure,
log_prior,
clus,
fc = 1,
cond_induce = "cases",
cf_sigma
)
}
\... |
13db35ecada560ff0fa515ac64a917c8947e71d7 | db78542ec83aa66cb8a543a94463bb99c58151e7 | /Pairwise T Test.r | 7d7cf1b6e2d7b32efc43042982289c02c86623fb | [] | no_license | chunhuayu/R-Learning | 59ee2567fb910c5124492da84603069ee5b9e2f1 | 36ede3bb562dca07029a8411e230b970e69f22e5 | refs/heads/master | 2020-05-09T14:59:05.094442 | 2019-06-29T03:06:52 | 2019-06-29T03:06:52 | 181,216,574 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 722 | r | Pairwise T Test.r | > library(multcomp, pos=4)
> library(abind, pos=4)
> AnovaModel.1 <- aov(days.mgraine.5 ~ group, data=Dataset)
> summary(AnovaModel.1)
Df Sum Sq Mean Sq F value Pr(>F)
group 3 76.7 25.5776 2.6472 0.04858 *
Residuals 435 4203.0 9.6621
---
Signif. codes: 0 '***' 0.001 '**'... |
c655d62cdfabb3c3f489aca2a380599b46a39f5f | 267aa85b975d3348c5557505298364d6f1b5b7f4 | /inst/shiny/server.R | a8ea84058d1e8b43166296a8f28387c1f1a983a1 | [] | no_license | matdoering/openPrimeRui | d2a6dad1038ddbe8e3541ccba6a2172271ccaf09 | 9b3f330bffff554986733cc85d2d40f7f8e2953f | refs/heads/master | 2021-01-19T19:40:13.861823 | 2020-08-14T07:52:24 | 2020-08-14T07:52:24 | 101,199,926 | 3 | 3 | null | null | null | null | UTF-8 | R | false | false | 2,270 | r | server.R | ##########
# Shiny server functionalities
##########
server <- function(input, output, session) {
##############
# define static variables for the shiny app:
source(system.file("shiny", "shiny_server", "extra_shiny_backend.R",
package = "openPrimeRui"))
#print("Require namespace test:")
... |
7dfd720ca0a1ffb5169432c6fbaff7d2ea1cbd26 | 7676c2d5b77b588adde0e787a501dac27ad8efcd | /work/r๋ฐ์ดํฐ๋ถ์_์์ ํ์ผ/์์ /2_01.R | c6fe40a04808b64ad6e2a62094ce9aa7f85cd1c6 | [] | no_license | bass4th/R | a834ce1e455ca2c018364a48ea8ba0fbe8bf95e9 | fb774770140a5792d9916fc3782708e09c12273d | refs/heads/master | 2020-05-05T09:02:52.210737 | 2019-04-06T19:48:37 | 2019-04-06T19:48:37 | 179,888,937 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 65 | r | 2_01.R | search()
install.packages("igraph")
library(igraph)
search()
|
7402953aca7d9e91934980cfa68591202944cd10 | cc00b5cdf7898fa1d960f09cecbced322443ecbf | /R/diff_days.R | 135dde6a05e7d1977252293e4ab2ce6cc2d65abc | [
"MIT"
] | permissive | edgararuiz-zz/maya | 6a7392da41a61ef36574267de6718823cc7d5494 | 51129990671b06d50d62435a4f99dce9098fa918 | refs/heads/master | 2022-01-06T15:39:44.970647 | 2019-05-12T16:15:54 | 2019-05-12T16:15:54 | 184,825,567 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,632 | r | diff_days.R | #' Number of days between two gregorian dates
#'
#' It determines the number of days between two gregorian dates. It works
#' independently from any R date/time function. An advantage of this function
#' is that it is accepts dates older than year 1 CE. It uses calendar and
#' not astronomical year numbering.
#'
... |
91d9a857c8631c68c10ea239b9f181ce5eb223b6 | e5ff475e7fec1c7cccebbd3eff284b8ba1f9ba53 | /man/nessy_examples.Rd | 6d0a023c225a62aaf03a5a6d82f84fc3097b276d | [
"MIT"
] | permissive | rpodcast/nessy | d4230125ed3f0fc460c6a78eaf7f6bf02cc794e7 | 8c2dd9e0cfe1f1ab29172d6c3e162b515ac60e1d | refs/heads/master | 2020-04-24T21:50:56.820584 | 2019-02-14T10:10:49 | 2019-02-14T10:10:49 | 172,291,748 | 0 | 0 | NOASSERTION | 2019-02-24T03:33:58 | 2019-02-24T03:33:57 | null | UTF-8 | R | false | true | 393 | rd | nessy_examples.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/examples.R
\name{nessy_examples}
\alias{nessy_examples}
\title{Get a NES example}
\usage{
nessy_examples(which = NULL)
}
\arguments{
\item{which}{The example to run. If empty, all the available examples are listed.}
}
\value{
A path to the ex... |
53b50e9a5da9bcda0227694892e65913c57920e7 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/untb/examples/volkov.Rd.R | 41e921d8a7b82389b89c069aa997dbca874f434c | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 521 | r | volkov.Rd.R | library(untb)
### Name: volkov
### Title: Expected frequency of species
### Aliases: volkov
### Keywords: math
### ** Examples
## Not run:
##D volkov(J=21457,c(theta=47.226, m=0.1)) # Example in figure 1
## End(Not run)
volkov(J=20,params=c(theta=1,m=0.4))
data(butterflies)
r <- plot(preston(butterflies,n=9... |
ff8500d0dd28ce8eae67523316740e491697b4ab | c2a6015d964e0a004fa4ac9c59df8aed039cc4fc | /man/knitAndSave.Rd | 6451628de8365c9fcbad4ae50637fc4e7f8ae8b0 | [] | no_license | cran/ufs | 27083e54b6e4c89f802c4de9218dbbd7c7d4260d | 74bcfb60160bced552d79d301b739bb965d1a156 | refs/heads/master | 2023-06-23T09:48:11.331297 | 2023-06-09T15:30:03 | 2023-06-09T15:30:03 | 145,907,951 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,686 | rd | knitAndSave.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/knitAndSave.R
\name{knitAndSave}
\alias{knitAndSave}
\title{knitAndSave}
\usage{
knitAndSave(
plotToDraw,
figCaption,
file = NULL,
path = NULL,
figWidth = ufs::opts$get("ggSaveFigWidth"),
figHeight = ufs::opts$get("ggS... |
558d71085f9678ca8c434a7828ceaaa268f5366b | 46ebd01f819e499506c5f16f44ea96556225172f | /Getting and Cleaning Data/Week 2/quiz.R | b3e0ae3af956f61fe72e1f205f958db77bfe3f5e | [] | no_license | peterchiappini/datasciencecoursera | 8ea7b0896fabde95e78f86cc04de13fd18193b3b | 9e40202af1bb177a88d7bc4152c02b3a26f76ae0 | refs/heads/master | 2023-06-15T01:45:18.294878 | 2021-07-16T22:46:51 | 2021-07-16T22:46:51 | 295,849,808 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,099 | r | quiz.R | library(httr)
library(sqldf)
# PROBLEM 1
oauth_endpoints("github")
myapp <- oauth_app("github",
key = "e60d1c464f658054d81a",
secret = "5fde2ac1529b22d3cf1d4b0b0a38005478a9ff3d"
)
# Get OAuth credentials
github_token <- oauth2.0_token(oauth_endpoints("github"), myapp)
gtoken <- ... |
6582dba5e2a9ae44dc25039011dd9ba71c93838b | 52d489c2491476428a9a0cd11b200c63be4794eb | /man/gen.arch.wge.Rd | 97f56f9f21146e92d006f518df1f2f50053c9865 | [] | no_license | cran/tswge | 2ffabc4794652937b86a701ec4772b2e07697531 | 435566d44f7652da48e9d257040fc78b47a08101 | refs/heads/master | 2023-04-01T22:52:04.649970 | 2023-01-31T12:10:02 | 2023-01-31T12:10:02 | 236,955,358 | 0 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,042 | rd | gen.arch.wge.Rd | \name{gen.arch.wge}
\alias{gen.arch.wge}
\title{Generate a realization from an ARCH(q0) model}
\description{Generates a realization of length n from the GARCH(q0) model (4.23) in "Applied Time Series Analysis with R, 2nd edition" by Woodward, Gray, and Elliott}
\usage{
gen.arch.wge(n, alpha0, alpha, plot = TRUE,sn... |
ce5e8a2b163db96e6ede8014d1b5691bbe2c2f29 | 08da636974bcd1dfd35c6265a0687c32c609571e | /R_coco/crawling_basic2.R | 10d2edde000c554d7f5800f3e3b11cf7682e73e0 | [] | no_license | edgestory/DS | 0698ac4d4fdc3958289b052aac40917efe35052e | 5d3c3ad8b0f4b24a73ac75b776448973ad3b149f | refs/heads/master | 2023-01-13T07:37:38.604708 | 2020-11-17T09:33:06 | 2020-11-17T09:33:06 | 33,327,820 | 0 | 0 | null | 2020-06-05T01:41:47 | 2015-04-02T19:31:16 | Jupyter Notebook | UTF-8 | R | false | false | 563 | r | crawling_basic2.R | library(stringr)
getwd()
data <- read.csv("final_data.csv")
head(data)
url_list <- data[,3]
length(url_list)
content <- c()
for ( i in 1:length(url_list)){
## try_error
if(class(try(b<-readLines(as.character(url_list[i]), encoding = 'UTF-8'))) == "try-error"){
b6 <- ""
content <- c(content,b6)
# next;
}else... |
d2bfd51b27ab75cfa8483beab7a3e994c7ccd4f4 | 5d690f159266b2c0f163e26fcfb9f9e17a0dc541 | /inlabru/R/bru.integration.R | 1808f6eba1f861f27e20a38e5488e45615f13ab3 | [] | no_license | albrizre/spatstat.revdep | 3a83ab87085895712d7109c813dcc8acb55493e9 | b6fc1e73985b0b7ed57d21cbebb9ca4627183108 | refs/heads/main | 2023-03-05T14:47:16.628700 | 2021-02-20T01:05:54 | 2021-02-20T01:05:54 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 21,121 | r | bru.integration.R | #' @title Generate integration points
#'
#' @description
#' This function generates points in one or two dimensions with a weight attached to each point.
#' The weighted sum of a function evaluated at these points is the integral of that function approximated
#' by linear basis functions. The parameter \code{region} ... |
1e8d3b75880fe43de8d9672687018f39169323e6 | 117936196834fbda370de297d6f5a77846bf45e9 | /old/testingHMSCv2/functions/data_wrangling_fx.R | 343931fb58d203db351a25e42c48732325300814 | [] | no_license | javirudolph/testingHMSC | a79dc2ffcdec967ed45d23e46151044d1365ab51 | 61c3e1b035b8095c45755833d2ab0ebc1179a6fb | refs/heads/master | 2021-06-16T04:27:22.878177 | 2021-03-11T18:46:51 | 2021-03-11T18:46:51 | 170,368,566 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 4,762 | r | data_wrangling_fx.R |
# Functions to modify and organize the dataframes from the VP output and get ready for plotting
doItAll_dataWrangling <- function(outPath, scenarioNum, indSites = FALSE){
if(indSites == TRUE){
richness <- readRDS(paste0(outPath, scenarioNum, "-metacomSim.RDS")) %>%
set_names(imap(., ~ paste0("iter... |
d1f8a2a6fe3926e788ca2176355b630d712e0d02 | 3e9f4fef38f2fbcee67db40525d2cf343d5dd264 | /R/Basics/02_check_data.R | 0ff1900287eb9b5b61abfbfe5071e1f1866dc35c | [] | no_license | LS-2ednar/statistics_cheatsheat | 727a9b2b376639ea3b03d4f17797db763cb84e57 | e525d33fe79c41c434c97665a22c8ee26aab11b3 | refs/heads/main | 2023-01-13T05:44:07.687749 | 2020-11-23T16:22:18 | 2020-11-23T16:22:18 | 315,372,424 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,200 | r | 02_check_data.R | # checking your data is crucial to do good analysis! Here some methods are shown
# install the mice packages to check your dataset for NA's and use md.pattern to
# find missing datapoints. IF you get all 1 and 0 below you are golden
install.packages('mice')
library(mice)
md.pattern(data)
# use str() to figure o... |
85791c243734d690afe620b923c765dfb3f77fc3 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/clues/examples/Maronna.Rd.R | 6f537fdb5a255577f52d5d24cb4a10cc0a461750 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 418 | r | Maronna.Rd.R | library(clues)
### Name: Maronna
### Title: The Maronna Data Set
### Aliases: Maronna maronna maronna.mem
### Keywords: cluster
### ** Examples
data(Maronna)
# data matrix
maronna <- Maronna$maronna
# cluster membership
maronna.mem <- Maronna$maronna.mem
# 'true' number of clusters
nClu... |
473e1fc4e6a85c0b943f58c4700d207258a3451d | bc7cb0d6281727d4283b8635143cec8e1c864287 | /man/coef.estimate.Rd | 0300cf7f52a44d19d16996f92175a363c36143d3 | [] | no_license | josue-rodriguez/GGMnonreg | e8cfb3e1db1c96c226e91705642227cf4a3ee7eb | 33ecd010df57525411261a08005a4d2f946327d3 | refs/heads/master | 2021-01-02T00:30:37.231210 | 2020-01-27T20:45:52 | 2020-01-27T20:45:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,145 | rd | coef.estimate.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/coef.GGM_bootstrap.R
\name{coef.estimate}
\alias{coef.estimate}
\alias{coef.GGM_bootstrap}
\title{Precision Matrix to Multiple Regression}
\usage{
\method{coef}{GGM_bootstrap}(object, node = 1, ci = 0.95, ...)
}
\arguments{
\item{object}{obje... |
5adfd8b8f8e0bca46f7f9160d4ba41de038ba923 | 11d4d6cb6be6f3a5b45c8470045ae6a7b03e11e9 | /app.R | 9e491ca2cd2c37a4d58b3a5c4bfed47cd09c14e6 | [] | no_license | tobias-heuser/classification-digitalisation-projects | 8a51aa619a18f61ccaa97e739cd914176bdbaca8 | ced2c1cd34c573486736545b3cdff69363990037 | refs/heads/main | 2023-04-02T21:28:48.430489 | 2021-03-27T01:08:42 | 2021-03-27T01:08:42 | 351,126,848 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,201 | r | app.R | # install.packages("ggdendro")
# install.packages("reshape2")
# install.packages("grid")
# install.packages("dendextend")
library(dplyr)
projects <- data.frame(
#id.s = c(1:58),
pro_involved = c("+C", "1D", "+D", "+C", "+C", "+D", "+D", "+D", "1D", "1D", "+C", "+D", "+C", "+D", "+C", "+C", "+C", "+D", "+C", "+C",... |
b80f1e2882f77666da50b18fc51bc4b90daa4372 | bf745c74482e237f777aa00c20e3bbf9f4cfa0bf | /EjBrandEval/R/process.Ej.cjs.r | 1a1f4c4689d7f665033ab6f30bf7bc57dd6c3058 | [] | no_license | jlaake/EjBrandEval | 5c3f11a2d3c3e320b56c555ab81db9afde3a9a6f | e284c94e4156139e46349d2ccfa57dfa206383df | refs/heads/master | 2021-01-18T21:56:57.349758 | 2016-05-24T22:09:59 | 2016-05-24T22:09:59 | 2,009,626 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,923 | r | process.Ej.cjs.r |
#' Prepares data for running RMark models
#' Prepares data by running process.data and make.design.data step for RMark.
#' Creates occasion-specific design data for groups and for occasions based on
#' platform used for re-sighting.
#'
#' @export
#' @param ej.list list that results from running \code{\link{e... |
f95e42650906c1a1c0a17119ddf31731761933af | 29891624cdb77ca6a43b683cc8d668612590e877 | /R/get.index.mat.R | dd4892439dd94debc5e183724e0515dd5532b228 | [] | no_license | kellijohnson-NOAA/saconvert | e8f3d0aa853cf58a050826ccdf4aa35804b1556e | d004f5cee8af1edb27fe8a15ffac41cfc1ac61d6 | refs/heads/master | 2022-07-07T16:04:06.041578 | 2022-01-16T15:41:23 | 2022-01-18T14:39:01 | 230,995,952 | 0 | 2 | null | 2021-07-09T17:24:02 | 2019-12-30T23:56:04 | R | UTF-8 | R | false | false | 1,134 | r | get.index.mat.R | #'
#'
get.index.mat<- function(x, cv, neff, first.year, nyears, catch.ages, survey.ages) {
n.ages = length(catch.ages)
last.yr <- first.year+nyears - 1
tmp.yrs <- as.numeric(rownames(x))
all.years = first.year-1 + 1:nyears
years.use.ind = which(tmp.yrs %in% all.years)
#if (tmp.yrs[length(tmp.yr... |
9f34517ce67308434ac19640406e6a3f254de609 | 246f3d934ab5aad0a6ea7f34e46ccf80cc11b8fd | /code/part_3.R | ff010ebf6494673b037041cb42b4fbe72aa48b09 | [] | no_license | ewong027/stats133-final-project | 944299a2678a4dcfde390ad22f71c39efa739389 | 6d4b76ba5e62bc25fffecdeb87c7878ae9c2d169 | refs/heads/master | 2020-06-10T08:26:11.924729 | 2015-12-10T21:28:27 | 2015-12-10T21:28:27 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,276 | r | part_3.R | # ======================================================================
# Part 3: Type analysis
# Description: Here we are looking at how the most common types of
# airplanes to crash changed over different decades.
# ======================================================================
# Note: must ... |
ef55269907ea1364f995fe54dd5d70dfd2902631 | 11dd3782354ca82cc5dfc6996d3b707e5f563010 | /man/taxa_rollup.Rd | 75c0a59c3bbfb27b84ff703b9c81f8fd7f48806f | [
"MIT",
"BSD-2-Clause"
] | permissive | pmartR/pmartRseq | 49010cceff1a173e924e7b467781cec86c0aaae6 | 75d573e528d14a6563a69be46c9a326c13423738 | refs/heads/master | 2020-12-31T07:33:06.348449 | 2018-02-09T00:39:18 | 2018-02-09T00:39:18 | 86,605,696 | 2 | 1 | BSD-2-Clause | 2018-02-09T00:39:19 | 2017-03-29T16:35:39 | R | UTF-8 | R | false | true | 1,314 | rd | taxa_rollup.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/taxa_rollup.R
\name{taxa_rollup}
\alias{taxa_rollup}
\title{Roll up data to a specified taxonomic level}
\usage{
taxa_rollup(omicsData, level, taxa_levels = NULL)
}
\arguments{
\item{omicsData}{an object of the class 'seqData' created by \cod... |
1d93e92dbd3dd1686ccb693d119e709fd4b4b213 | 4200785bd7e0ba6c0b83bea00357cefd9a550510 | /plot1.R | 187076326df51a304e94b178d9d07fd78c6fe68b | [] | no_license | meganminshew/ExData_Plotting1 | 245d7efebb2851db6c4c3c78823ae374d9bb842d | 271c269b6c02fab1eab5b0cf2e84a7dcfa99f33e | refs/heads/master | 2021-01-18T05:06:44.038840 | 2014-05-09T15:54:30 | 2014-05-09T15:54:30 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 602 | r | plot1.R | ## read the data from the working folder
dt <- read.delim("household_power_consumption.txt", header = TRUE, sep = ";", colClasses="character")
## class the date
dt$Date = as.Date(dt$Date, format = '%d/%m/%Y')
## filter for the 2 days
dt <- subset(dt, dt$Date == "2007-02-01" | dt$Date == "2007-02-02")
## get the attribu... |
2c966ae010fe65346653de89036a6f9bf3b7a7fa | cfde857379a0b0a9b216cf08fa99449045bf4997 | /supervised/generative_learning/naive_bayes/golf_play.R | 6cefa80538c7b71f131bdc8c9b8fe40f380f79f4 | [] | no_license | sureshpodeti/Machine-Learning | c0a1922e77bba2b6cbbd596ae075c99a287ee7f2 | 32922a1c307c9666b0143e98c975e5a0c0ce26b4 | refs/heads/master | 2021-06-02T06:27:41.698147 | 2020-06-29T03:43:03 | 2020-06-29T03:43:03 | 131,320,257 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 677 | r | golf_play.R | # Load the data into data frame
df <- read.csv('/home/podeti/Desktop/AI/Machine-Learning/Data/golf_play.csv')
n <- ncol(df)
# convert dataframe into matrix
data <- data.matrix(df)
# split the data into train and test data
library(caret)
partitionIndex <- createDataPartition(data[, n], p=0.7, list=FALSE)
data_train <... |
a7174d367bb2a6581fc54b6e7ae5cc367675ee19 | 5c1426dddbe4f3e13929b0f9c44569be2e0885fa | /man/get.run.par.Rd | 73b80fdd5c3f773a0070c5a07070c3389cbd41c1 | [] | no_license | cran/dynatopmodel | 1ae2d5695d24be40f0f5c1f5e30526fd8bfb06ed | 0d7d074b022452f925c8de4271d4a843882ac84c | refs/heads/master | 2021-01-21T21:54:23.182728 | 2018-01-19T13:37:01 | 2018-01-19T13:37:01 | 19,747,316 | 1 | 0 | null | null | null | null | UTF-8 | R | false | true | 837 | rd | get.run.par.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/defs.r
\name{get.run.par}
\alias{get.run.par}
\title{get.run.par
Initalise model run parameters. Note this function is maintained for backward compatibility only}
\usage{
get.run.par(tms = NULL, dt = NULL, units = "secs", ...)
}
\arg... |
0e8463e2711920068daedfc3b4a7d62e086523e7 | 29587cb4ef25b1db3338bd2cc72d944da87d4f1d | /alt-as-sin-2016.R | a45891da13f1aa2bd78fe250770871cbd43efbcb | [] | no_license | kleinschmidt/brewnotes | 687ddaed6ebce32083eedb64cdaf4ad5abd70b4b | 30b71f81ecb74a05377369336cec723b701987bc | refs/heads/master | 2023-02-27T16:27:26.756927 | 2021-01-30T02:32:59 | 2021-01-30T02:32:59 | 334,311,379 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 278 | r | alt-as-sin-2016.R | library(magrittr)
devtools::load_all()
brewnotes::strike_decoc_topoff_sparge_gal(grain_lbs = 10.5,
mash_thickness = 1.5) %T>%
print() %>%
brewnotes::gal_to_lbs() %>%
`+`(2.3) # tare weight on bucket
|
e946b8dbea5d26eee26f9718c97e70dcb5ae60a0 | d1aba4f6a11564cc8f1f9d3b64801cb916a27687 | /plot2.R | beb3099ebaab548178f56f1ee8c35a751984a99c | [] | no_license | dvsdimas/exploratory-analysis-project-2 | 9a1e412eeef661c69a13acaade4448d5957e1b91 | fee9e4f5275b6c42da843e953d7ca1d9e4fff9ae | refs/heads/master | 2021-06-10T15:37:11.930301 | 2016-12-12T17:50:44 | 2016-12-12T17:50:44 | 76,126,186 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,195 | r | plot2.R | require(dplyr)
get_data <- function() {
data_folder <- file.path(getwd(), "data")
file_source <- file.path(data_folder, "Source_Classification_Code.rds")
file_summary <- file.path(data_folder, "summarySCC_PM25.rds")
if(!dir.exists(data_folder)) {
dir.create(data_folder)
... |
10d50bfeb4a12fa7dba26f595e7305040d3662cc | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/LilRhino/examples/Codes_done.Rd.R | f4a95c5e38e13a6d2f9ef080706ebdb3f1e6df23 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 188 | r | Codes_done.Rd.R | library(LilRhino)
### Name: Codes_done
### Title: For announcing when code is done.
### Aliases: Codes_done
### ** Examples
Codes_done("done", "check it", sound = TRUE, effect = 1)
|
ad81dc83bbe0ccc31e5698c0fa7c49a80bb77254 | 60d84fe954bc0dbcf75d254991ec730dd71e67f3 | /Rfiles/tables/table_3_html_ETo_tower_MOD16.R | bd121b996adc6edcce028869e1ca32cfba8092c3 | [] | no_license | tbiggsgithub/SEBAL_ET_CA | 024cd94b604684ec10807380b1b0685d10e2370b | 99b9d5349aba55c0109de15a5bc85b934a96dba7 | refs/heads/master | 2021-01-01T06:04:57.573882 | 2015-02-15T20:56:15 | 2015-02-15T20:56:15 | 30,841,717 | 4 | 3 | null | null | null | null | UTF-8 | R | false | false | 627 | r | table_3_html_ETo_tower_MOD16.R | library(htmlTable)
outdir.tables = "G:/mydocuments/SDSU/research/CA/ET_MOD16_SEBAL_towers/writeups/tables/"
sourcedir = "G:/mydocuments/SDSU/research/CA/ET_MOD16_SEBAL_towers/Rfiles/plots/"
sourcefile = "plot_ts_PET_MOD16_tower_multiple_in_one.R"
names(statsout2) = c("Tower","MOD16","Error %")
outhtml = htmlTable(st... |
be4bb64216599097635503cb7ba91fa1e7b98088 | 466a14350411044a071faa1702294d06b1543edf | /man/rattle.print.summary.multinom.Rd | 587b8520abefb9f93134d22672650e4d101a0d2b | [] | no_license | cran/rattle | 8fc67846c6dac6c282e905fe87ff38f0694056da | 3875c10d0ae6c7a499d918bc501e121861067e06 | refs/heads/master | 2022-05-02T14:29:57.688324 | 2022-03-21T12:10:02 | 2022-03-21T12:10:02 | 17,699,048 | 18 | 33 | null | null | null | null | UTF-8 | R | false | false | 694 | rd | rattle.print.summary.multinom.Rd | \name{rattle.print.summary.multinom}
\alias{rattle.print.summary.multinom}
\title{
Print information about a multinomial model
}
\description{
Displays a textual reveiw of the performance of a multinom model.
}
\usage{
rattle.print.summary.multinom(x, digits = x$digits, ...)
}
\arguments{
\item{x}{An rpa... |
17466ed5d0967ca74826fc7a5f5e32f395fdae8a | 599c2cf0ad1b158138c78b5c6c4c2804bbeb45d0 | /R/oneHot.R | 175ce7816e63c05034ced0905f0b8629e584de88 | [] | no_license | tlarzg/rtemis | b12efae30483c52440cc2402383e58b66fdd9229 | ffd00189f6b703fe8ebbd161db209d8b0f9f6ab4 | refs/heads/master | 2023-07-07T20:53:43.066319 | 2021-08-27T03:42:19 | 2021-08-27T03:42:19 | 400,347,657 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 736 | r | oneHot.R | # oneHot.R
# ::rtemis::
# 2019 E.D. Gennatas lambdamd.org
#' One hot encoding
#'
#' One hot encode a vector or factors in a data.frame
#'
#' A vector input will be one-hot encoded regardless of type by looking at all unique values. With data.frame input,
#' only column of type factor will be one-hot encoded. This func... |
b2b381831832a5baffc1cc1f3161fcbad9b25610 | e49c37520df710db3c236c7a764dcad107332540 | /plot4.R | 881daededcd8f6cf2763621e382b36d8a426935f | [] | no_license | gvaljak/ExData_Plotting1 | 61e53841a3d51a37ab9043a1ec0b99f2e3e61e6b | fa0bbe6e85f9dedfba19094459bedda31e1dbf58 | refs/heads/master | 2021-05-31T21:19:27.594918 | 2016-04-07T10:05:31 | 2016-04-07T10:05:31 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,592 | r | plot4.R | #read data. values "?" are intrepreted as NA. file household_power_consumption.txt should be in the working directory
raw <- read.csv("household_power_consumption.txt", sep=";", na.strings = c("?"))
#remove NA from data
clean <- na.omit(raw)
#convert Date and Time from character vector to date and time
clean$T... |
a3e27717e3be0754676b1928cc961250fa829387 | a7840bbed633bf24e8040d9a28fb87b48190483a | /man/CreateFlowChart.Rd | 356bc63944472e66d9adac510e06c632b3136a20 | [
"MIT"
] | permissive | DavideMessinaARS/CreateFlowChart | 1a1abf55564bdd186e688cc60d3dfca6dd353e0e | 9fb860905f2e458b650d0dfcf8786b8f3b9fc90b | refs/heads/main | 2023-03-04T14:54:55.582627 | 2021-02-16T09:59:01 | 2021-02-16T09:59:01 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 1,279 | rd | CreateFlowChart.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CreateFlowChart.R
\name{CreateFlowChart}
\alias{CreateFlowChart}
\title{'CreateFlowChart'}
\usage{
CreateFlowChart(dataset, listcriteria, weight, strata, flowchartname)
}
\arguments{
\item{dataset}{input dataset to work with}
\item{listcrite... |
501be72617809c06bd1e82c15d72e7a66f91fde7 | 275791c3aad442f01680b1ef443ce1b615008ef2 | /day24.R | 76b7f0e3304e6007f791cbe55a1aeb50f81244ee | [] | no_license | mdequeljoe/aoc2020 | 429532169b7413d976dd5294c55d16adc4bae14b | 31483376883c538ce4edcbfff4c946fb35d1ad71 | refs/heads/master | 2023-02-15T17:42:01.100379 | 2021-01-10T12:50:12 | 2021-01-10T12:50:12 | 318,455,525 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,554 | r | day24.R |
hex <- function(point, coord) {
diff <- list(
nw = list(d = c(1, -1), link = 'n'),
n = list(d = c(1, 1), link = 'ne'),
ne = list(d = c(0, 1), link = 'se'),
se = list(d = c(-1, 1), link = 's'),
s = list(d = c(-1, -1), link = 'sw'),
sw = list(d = c(0, -1), link = 'nw')
)
res <- setNames... |
cb2da27a49e5c4390e22a539e189e73195f0a6e5 | 54182c6ac646b9c4aecc75fdcdc290cd67e0beb8 | /R/layeropts.R | 1f127fd6e19b7bfd70a15922f222bdb5ee0b37e4 | [
"MIT"
] | permissive | vincenzocoia/copsupp | 92ed0b9a00cf77d8c4146b841614d8fe6da3b70c | 9b92e11670aff9a7b6ef8365fc2c9b3a8c904923 | refs/heads/master | 2021-03-19T16:01:28.754408 | 2020-08-21T16:04:50 | 2020-08-21T16:04:50 | 46,912,656 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,310 | r | layeropts.R | #' Fitting options for a new Vine Layer
#'
#' When fitting a new layer(s) to a vine, use this function to specify
#' "known" components of the new layer(s), as well as
#'
#' @param ntrunc Truncation level. Could be a vector corresponding to the
#' truncation level for the variables \code{var} or \code{G[1, ]}.
#' @note... |
4f627b5ea3f9bb63c5bf71372d2a1f246a25bc39 | b211639b9d1b4ca253df986eb2344d029bd415d5 | /Problem Set 1/Scripts/summarizePlotQuartet.R | a3e2dac5538ac778447ab9ce6a8c4da0787be6ad | [] | no_license | cortbreuer/ENGRD-2700-Problem-Sets | ca318aef280f3e872f2199139b46439862572e6d | 3738314fb760e22d4eb33fe2f320d43d54e91324 | refs/heads/master | 2020-07-17T23:30:00.698135 | 2019-12-14T00:39:55 | 2019-12-14T00:39:55 | 206,123,955 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,273 | r | summarizePlotQuartet.R | #load libraries
library(tidyverse)
library(knitr)
library(kableExtra)
library(gridExtra)
theme_set(theme_bw())
#import quartet data set
quartet <- read.csv("Data/Quartet.csv")
#summarize data by mean, median, standard deviation and show in table
colNames <- c("X1", "Y1", "X2", "Y2", "X3", "Y3", "X4", "Y4")
quartetMe... |
f8969f12393499df7171b17723b24692b556f93f | 69cba8c80765e2c57d5cdf1901833a697bfaa05f | /PrisonerProblem.R | 7e686fc899e5ca521352f50990cca6f495b24854 | [] | no_license | jtownball/Sandbox | d81453e2ade709b74fa0ef44efe0ee7b8b273c99 | cea575d424627b0aeb7e355006b5cc32690e641f | refs/heads/master | 2023-05-13T04:58:15.579487 | 2023-05-07T03:09:14 | 2023-05-07T03:09:14 | 14,943,552 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,013 | r | PrisonerProblem.R | # The director of a prison offers 100 death row prisoners,
# who are numbered from 1 to 100, a last chance. A room
# contains a cupboard with 100 drawers. The director randomly
# puts one prisoner's number in each closed drawer. The prisoners
# enter the room, one after another. Each prisoner may open and
# look i... |
5b0766f63b58293af915d9877f3c081c53f4187c | e68e99f52f3869c60d6488f0492905af4165aa64 | /tests/testthat/test-jit-ops.R | 5d52159a191c6711fa5398b6582bfed5425fb7dc | [
"MIT"
] | permissive | mlverse/torch | a6a47e1defe44b9c041bc66504125ad6ee9c6db3 | f957d601c0295d31df96f8be7732b95917371acd | refs/heads/main | 2023-09-01T00:06:13.550381 | 2023-08-30T17:44:46 | 2023-08-30T17:44:46 | 232,347,878 | 448 | 86 | NOASSERTION | 2023-09-11T15:22:22 | 2020-01-07T14:56:32 | C++ | UTF-8 | R | false | false | 1,480 | r | test-jit-ops.R | test_that("can access operators via ops object", {
# matmul, default use
res <- jit_ops$aten$matmul(torch::torch_ones(5, 4), torch::torch_rand(4, 5))
expect_equal(dim(res), c(5, 5))
# matmul, passing out tensor
t1 <- torch::torch_ones(4, 4)
t2 <- torch::torch_eye(4)
out <- torch::torch_zeros(4, 4)
ji... |
2303897d89b41f1409a1fc7af923f5041d079949 | 999f6296b3102c5374af78e8f19f783db7ae0f22 | /R/testing_fun.R | 911fa7dcb205da9c24b80534abd5138fb51e5454 | [] | no_license | felix28dls/ddCt_QPCR_Analysis | b052a4beb308e7650cda03f864829e6266c1ca20 | 0539f31edb2b3b506de0a9c2db90a01190fb0e3c | refs/heads/master | 2020-06-16T20:55:48.974964 | 2019-07-07T22:28:42 | 2019-07-07T22:28:42 | 195,700,929 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 11,266 | r | testing_fun.R | #' Statistical testing of PCR data
#'
#' A unified interface to different statistical significance tests for qPCR data
#'
#' @inheritParams pcr_ddct
#' @param test A character string; 't.test' default, 'wilcox.test' or 'lm'
#' @param ... Other arguments for the testing methods
#'
#' @return A data.frame of 5 columns in... |
f3480014bf6aaf416aacf29583e28961c45e4880 | 5e42a668e417fd55fe28ecee719c759016f963b9 | /tests/testthat/test-expect_s3_class_linter.R | d071a8f7748c2707bb09366cc370f4a402eda934 | [
"MIT"
] | permissive | cordis-dev/lintr | 2120e22820e8499ca3066fa911572fd89c49d300 | cb694d5e4da927f56c88fa5d8972594a907be59a | refs/heads/main | 2023-08-05T08:50:42.679421 | 2023-07-25T13:21:29 | 2023-07-25T13:21:29 | 225,583,354 | 0 | 0 | NOASSERTION | 2019-12-03T09:41:30 | 2019-12-03T09:41:30 | null | UTF-8 | R | false | false | 3,306 | r | test-expect_s3_class_linter.R | test_that("expect_s3_class_linter skips allowed usages", {
linter <- expect_s3_class_linter()
# expect_s3_class doesn't have an inverted version
expect_lint("expect_true(!inherits(x, 'class'))", NULL, linter)
# NB: also applies to tinytest, but it's sufficient to test testthat
expect_lint("testthat::expect_t... |
841ff67402ac255d1f04b5e773f66aa5a849cea9 | 9aafde089eb3d8bba05aec912e61fbd9fb84bd49 | /codeml_files/newick_trees_processed/8734_0/rinput.R | ac611a14df07c87fa836e4a652250d6ba090eb24 | [] | no_license | DaniBoo/cyanobacteria_project | 6a816bb0ccf285842b61bfd3612c176f5877a1fb | be08ff723284b0c38f9c758d3e250c664bbfbf3b | refs/heads/master | 2021-01-25T05:28:00.686474 | 2013-03-23T15:09:39 | 2013-03-23T15:09:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 135 | r | rinput.R | library(ape)
testtree <- read.tree("8734_0.txt")
unrooted_tr <- unroot(testtree)
write.tree(unrooted_tr, file="8734_0_unrooted.txt") |
7b3431e0aea653b152481ca397c752f0e6f90796 | 92220d3bc952901e2423745771de1725c34e5c86 | /dataproc/workerMetrics.R | 054fa771da9f986dfbf1cf1e9a196e5186bcd943 | [] | no_license | laroyo/watsonc | 9489605d94c1336a82350a2dc4494e54374dc624 | a55b62945ed75b85e81ec5be453ccb8a3edd6a25 | refs/heads/master | 2016-09-05T23:13:48.119320 | 2015-06-17T10:21:34 | 2015-06-17T10:21:34 | 7,225,727 | 5 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,674 | r | workerMetrics.R | #!/usr/bin/Rscript
## Read file 90-sents-all-batches-GS-sentsv3.csv and applies the filters.
## The filter output is the same as 90-sents-all-batches-CS-sentsv3.csv (Dropbox/data/CF-Results-processed/)
source('/var/www/html/wcs/dataproc/envars.R')
library(XLConnect)
source(paste(libpath,'/db.R',sep=''),chdir=TRUE)
... |
71b6b358012ba21845bc2ea6c118d40c863fede7 | c5904577c015ffd7254fef31eae73484aa0fc6a7 | /fitAndCompareModels.R | 92efad90591a072c5aa8dcb8729cc1260f681ad6 | [] | no_license | RetoSchmucki/SURPASS_WP1 | a11e56a6eaa79d8b5e78f9bf9db42715e6514683 | e103caea6b83dbb009511954acd70b96db19653c | refs/heads/main | 2023-06-11T03:03:08.761942 | 2021-07-01T10:55:35 | 2021-07-01T10:55:35 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,490 | r | fitAndCompareModels.R | #### fit models to estimate temporal trends in species' distributions ####
library(occAssess)
library(raster)
library(reshape2)
library(plyr)
library(dplyr)
library(ggplot2)
library(gridExtra)
# Load sparta
library(sparta)
## setup model grid
shp <- raster::shapefile("C:/Users/Rob.Lenovo-PC/Documen... |
9d1c235208299a5fa3fd5306ae06bf90553b82c7 | 6ceab1bf9c435b523d2f8e7e9440da39770d741b | /R/f7List.R | 4cc4cf33559ffc26ec8bba3b9af7a2d393c230b4 | [] | no_license | RinteRface/shinyMobile | a8109cd39c85e171db893d1b3f72d5f1a04f2c62 | 86d36f43acf701b6aac42d716adc1fae4f8370c6 | refs/heads/master | 2023-07-25T16:28:41.026349 | 2022-11-25T17:04:29 | 2022-11-25T17:04:29 | 139,186,586 | 328 | 92 | null | 2023-03-26T05:58:53 | 2018-06-29T19:13:06 | R | UTF-8 | R | false | false | 18,734 | r | f7List.R | #' Create a framework 7 contact list
#'
#' @param ... Slot for \link{f7ListGroup} or \link{f7ListItem}.
#' @param mode List mode. NULL or "media" or "contacts".
#' @param inset Whether to display a card border. FALSE by default.
#' @export
#'
#' @examples
#' if (interactive()) {
#' library(shiny)
#' library(shinyMobile... |
f9d6eedae94927a0bb00ea0ad3abaa3804a18df4 | 00f3affa6100239eba6a49728cbcaa5e03e8c4d2 | /code/mouse/R/gtf-munging.R | 49da952cdd035287cb456b654fe4c9117b58aef1 | [] | no_license | nehiljain/pgi-analysis | 72ae7953cc7797e8db92806deb2ec2afaf94fc44 | 1f31621fcc21ce15c91411b5369d00a053debcda | refs/heads/master | 2021-01-10T21:06:34.180321 | 2015-04-29T17:48:06 | 2015-04-29T17:48:06 | 17,648,428 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,227 | r | gtf-munging.R | rm(list=ls())
library(plyr)
library(dplyr)
library(stringr)
# library(rattle)
# This script loads the GTF file for mouse, creates a header, munges the strings to be made useful, converts to a dataframe and writes it to RData and CSV Files
# change the path to point to the GTF file from ensemble ftp://ftp.ensembl.org/... |
95aae3708fcb06bb712045fd5be75341be27389f | 0ba374b61d485f17ae45bcf6033d7deae7a84925 | /Laura_Pipeline/Clean_Lineage_genes.R | 155ca6b9386301be7bd8026338dd250de34adf4c | [] | no_license | tallulandrews/LiverTumouroidsScripts | cb6cd9ec4dbbb6dd51864ec5f6cb5cc3901cc66a | ed7e4a7a30912076cd91e208a485df4d47657e8a | refs/heads/master | 2022-03-02T18:07:38.553300 | 2022-02-28T11:30:35 | 2022-02-28T11:30:35 | 232,075,946 | 2 | 2 | null | null | null | null | UTF-8 | R | false | false | 2,311 | r | Clean_Lineage_genes.R | # Lineage markers
Chol_lineage <- read.table("/nfs/users/nfs_t/ta6/Collaborations/LiverOrganoids/Markers_130418_Chol.txt", header=TRUE)
Hep_lineage <- read.table("/nfs/users/nfs_t/ta6/Collaborations/LiverOrganoids/Markers_130418_Hep.txt", header=TRUE)
Hep_both <- Hep_lineage[ grepl("Prog", Hep_lineage[,2]) & grepl("He... |
d14297b6a9e14b0a7592ad409fb7941444c7ecdd | ecb38a2803f102e08c59813270d638111d41d10c | /FindFeaturesFrom PCa.R | c01006a442138112e920782062f50c1d807bd555 | [] | no_license | WeichselRiver/stamps | 52f0badb23d386a16b3d68cb1b74a6717ca9b604 | 271f241b12322dfdcdd6f386599dd140689177b8 | refs/heads/master | 2022-04-09T15:50:12.575177 | 2020-01-25T08:05:30 | 2020-01-25T08:05:30 | 115,778,314 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,513 | r | FindFeaturesFrom PCa.R | # Stamp Recognition using PCA
#.libPaths(c("C:/Daten/RStudio/R-3.3.2/library", "C:/Daten/R-3.1.2/library" ))
library(EBImage)
library(dplyr)
library(readxl)
t1 = read_excel("StampList.xlsx") %>%
dplyr::filter(bild == "Ziffern im Kreis")
pic_prep = function(x) {
x1 = EBImage::readImage(x) %>%
resi... |
f8e69d40582b2275ee2c455c7656738d0aefc510 | 9fe45af18aeb00a6de72d11f15916cd652913dd5 | /dmeas.R | 691598785dacd7d6c5bf9c8e807d8b09275050dc | [] | no_license | dkenned1/KennedyDunnRead | 804929ef873aaa1c4dbce00ff2ae329ab02a2292 | 0acd499f9c94a8d63ffbafbbb585a18179f44e3e | refs/heads/master | 2020-12-30T09:11:41.294172 | 2017-08-15T17:04:47 | 2017-08-15T17:04:47 | 100,393,887 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,137 | r | dmeas.R |
dmeas <- Csnippet("
double tol=1.0e-17;
double DetectionLimit=2;
double probit_beta_0= -2.206;
double probit_beta_1= 1.555;
double vcn_beta_0= 1.127;
double vcn_beta_1= -0.151;
double lVirusConc = log10(V/D +1);
double ProbitValue=probit_beta_0 + probit_beta_1*lVirusConc;
double ... |
8eb3a39cd449bd8043476e1668e39560dcad7c17 | 597f9a80945008773bfc3fde0c333913fa17395b | /weatherType2Table.R | 767d26ebc1b01795e0f749b6f6744c120aaa075c | [
"MIT"
] | permissive | drmingle/Wallmart-II | 0e97cb4775430dd1a0dcbca522f164773d5d0a1c | a8a262e5e5968fae0e4691fd0272c084b913ba53 | refs/heads/master | 2021-01-15T13:13:35.495976 | 2015-05-25T01:43:57 | 2015-05-25T01:43:57 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 342 | r | weatherType2Table.R | weatherType2Table <- function(weatherSting){
types2check <- c("TS", "GR", "RA", "DZ", "SN", "SG", "GS", "PL", "FG", "BR", "UP", "HZ", "FU", "DU",
"SS", "SQ", "FZ", "MI", "PR", "BC", "BL", "VC")
logicalString <- sapply(types2check, function(type){
return(grepl(type, weatherSting))
})
re... |
6ac06b24a9c899a953bae240af128fc5896bdc74 | 2a7e77565c33e6b5d92ce6702b4a5fd96f80d7d0 | /fuzzedpackages/gaston/man/lik.contour.Rd | 54580cbc923db67a21e091b8f5ee5bb723890f43 | [] | no_license | akhikolla/testpackages | 62ccaeed866e2194652b65e7360987b3b20df7e7 | 01259c3543febc89955ea5b79f3a08d3afe57e95 | refs/heads/master | 2023-02-18T03:50:28.288006 | 2021-01-18T13:23:32 | 2021-01-18T13:23:32 | 329,981,898 | 7 | 1 | null | null | null | null | UTF-8 | R | false | false | 1,574 | rd | lik.contour.Rd | \name{lik.contour}
\alias{lik.contour}
\title{ Contour plot for two parameters likelihood }
\description{ Create a contour plot (superimposed with a heat map) }
\usage{ lik.contour(x, y, z, levels = NULL, nlevels = 11, heat = TRUE, col.heat = NULL, ...) }
\arguments{
\item{x, y, z}{ As in \code{contour} }
\item{... |
d7825648a216034df4de0ff301cf2b77324cc1ec | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/rms/examples/orm.fit.Rd.R | a5e28f55cfe2822061097c4bbf2911481fb11f97 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 356 | r | orm.fit.Rd.R | library(rms)
### Name: orm.fit
### Title: Ordinal Regression Model Fitter
### Aliases: orm.fit
### Keywords: models regression
### ** Examples
#Fit an additive logistic model containing numeric predictors age,
#blood.pressure, and sex, assumed to be already properly coded and
#transformed
#
# fit <- orm.fit(cbind... |
1fd6fba9a67b47c7eeffc9f4c8dca333741c9257 | e6af6862edb1e783a00cb51a79a35a4496ffdc79 | /preprocessing/sample-data-test.R | c3220e15f332286aea698feafa0701a2aba3dabd | [] | no_license | jodeleeuw/219-2020-eeg-analysis | 19fc9b13305ade1dd79bad4541dc6e21e0f93048 | c3d5bb2811b53ee09bb69df4a478be5d46a31173 | refs/heads/master | 2023-07-27T06:40:57.453364 | 2021-09-13T12:21:17 | 2021-09-13T12:21:17 | 238,238,068 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 420 | r | sample-data-test.R | library(readr)
library(dplyr)
test_data <- read_csv('data/behavioral/longtest_csv.csv')
trials <- test_data %>% filter(phase=="test") %>% group_by(audio_type, match_type) %>% summarize(n=n())
trials <- test_data %>% filter(phase=="test") %>% group_by(audio_type, match_type, stimulus) %>% summarize(n=n())
trials <- te... |
b6391ef1ec7958a6eadc5d373999b5e67c3f935f | d3da6daa98914ca5e140930257c7e4f8f34fb7d2 | /algorithm/iterative_cmi_greedy_flexible_parallel.R | 3e7b9a775a6bc97ce045f40ad9d8af4ec1d47d5e | [
"MIT"
] | permissive | ylincen/CMI-adaptive-hist | 0a836051910f9def29818be000f96b3e8011613f | 264e65e55da0e3796d06ccb6e75147c7c66a6bf1 | refs/heads/main | 2023-03-27T02:04:33.397777 | 2021-03-25T09:57:24 | 2021-03-25T09:57:24 | 327,953,841 | 4 | 2 | null | null | null | null | UTF-8 | R | false | false | 3,895 | r | iterative_cmi_greedy_flexible_parallel.R | iterative_cmi_greedy_flexible_parallel = function(data, eps = 0, Kmax = 0, v=F, isCat = c(), max_num_updating = 20, cores=1){
require("parallel")
cores = min(cores, detectCores()) ## check if number of available cores is sufficient
# transform the categorical data into 1,2,3,...,(number of categories)
if(length... |
049f656f4085780fa01ca65a77121b8bec286ee2 | 00f898ae9d13abce71fb6680b85955487f2642f7 | /R/est_lucid.R | fcb94f147a088fc387d6503ddd0a8c23121fa976 | [] | no_license | cran/LUCIDus | 6e912853c0e7a801836164af8e917b4cef47c7c6 | 6370b1e66496f3c81a9671a9e8f8a5a38a98459c | refs/heads/master | 2022-11-23T09:05:57.209899 | 2022-11-08T09:10:02 | 2022-11-08T09:10:02 | 162,729,398 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 19,644 | r | est_lucid.R | #' @title Fit LUCID model to conduct integrated clustering
#'
#' @description The Latent Unknown Clustering with Integrated Data (LUCID) performs
#' integrative clustering using multi-view data. LUCID model is estimated via EM
#' algorithm for model-based clustering. It also features variable selection,
#' integra... |
5b3acce235137988b86a7a8a43702bc3fb806162 | 5385cba7f1961943ef93a09e79372bd8ae4ce787 | /plots/plot1.R | 374ff822770a33ca1c31b8f4c40ca72609d8f20e | [] | no_license | Deano24/ExData_Plotting1 | 1961584e9e6fe868635d85600b1543d0187c667e | 2360b2a97e1a1605979d86e2f5a3a8023ab9edba | refs/heads/master | 2021-01-16T21:34:53.529466 | 2014-07-13T03:48:39 | 2014-07-13T03:48:39 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 656 | r | plot1.R | #Reading in the data
data = read.table("household_power_consumption.txt", sep=";",header=TRUE, row.names=NULL,na.strings="?")
#Formatting the data field
data$Date <- as.Date( as.character(data$Date), "%d/%m/%Y")
#Subsetting the data
subsetdata <- subset(data, Date >= as.Date("2007-02-01") & Date <= as.Date("2007-02-02"... |
74e7b6ffe42f87b6850108f46b11b02cb9653a00 | abb7fe666cf31c5c77a0632b84090be758637b53 | /tests/testthat/can_download.R | bb7865361141b0448140ba40c126469b78b2ab5d | [] | no_license | tanio003/argoFloats | d9a220673cec60ff06801f7a1ff939ca3495ee5d | 510100fc42e80a468920fc6e3510abbf4d10d64e | refs/heads/main | 2023-04-02T16:50:57.951812 | 2020-07-23T16:51:52 | 2020-07-23T16:51:52 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 620 | r | can_download.R | ## Do we have permission to download files? (NOT EXPORTED)
## @param destdir character value giving directory into which to download files
## @return logical value indicating whether this user has permission to download fies to ~/data/argo
canDownload <- function(destdir="~/data/argo")
{
## FIXME(dek): add username... |
84cc0173b46a7922b05f31f1614ff0498174b847 | 94129bfca0b1d8a9508be81b9ac6faf96bd4e07b | /R/sessionQuestions.R | 38b4439a05fd99f67bedc43e6478aa3a6809527a | [] | no_license | lgnbhl/polyglot | 8601b68fc1a81c82195e4b4fd258e975db915d59 | 665c05cdbfc982622f223e09b215297e4d8f7029 | refs/heads/master | 2021-12-26T04:32:34.540666 | 2021-10-10T10:43:53 | 2021-10-10T10:43:53 | 126,710,810 | 25 | 3 | null | 2021-09-28T20:38:45 | 2018-03-25T15:20:23 | R | UTF-8 | R | false | false | 9,031 | r | sessionQuestions.R | #' Ask questions in the interactive environment
#'
#' This function will print the questions in the interactive learning environment.
#'
#' @details
#' This function reads the selected dataset and print the first row of its first
#' colomn, i.e. the question. Then it presents to the user a menu, which gives him
#' mult... |
5dc3bf85aa60f4132f471b0e16210d09e1133f49 | 356373c526fe9d3c490a3b20f324a85b9aca8e3b | /DevSF/archived/man_for_v1/plot.mcmckingui0.Rd | d39de59eafa696f61c8e7c99d32a3128614891c4 | [] | no_license | zhenglei-gao/StudyKin | 89885e0a5cefd4cc443deee37c6173f0c94f1543 | 7f1adca875f93e5af4c718f0ce9f7bdc2f127df8 | refs/heads/master | 2021-01-25T08:43:08.087744 | 2013-04-08T23:28:45 | 2013-04-08T23:28:45 | 8,031,257 | 2 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,795 | rd | plot.mcmckingui0.Rd | \name{plot.mcmckingui}
\alias{plot.mcmckingui}
%- Also NEED an '\alias' for EACH other topic documented here.
\title{
%% ~~function to do ... ~~
S3 method to plot for calss 'mcmckingui'
}
\description{
%% ~~ A concise (1-5 lines) description of what the function does. ~~
}
\usage{
plot.mcmckingui(object, fname1, ... |
5a829b578b660e37942580a871296ddf71061052 | 4866242878090b27f05c1af37a192f3c730a5476 | /bvi_plot.R | b39e1d833b7bc2b09a072fcd57f061d98e83d31e | [] | no_license | jcvdav/bvi | d2afe03f3ca7ff29c8bd959f56a824e67adf2e0e | ef049421ab9dff03878776abd0f276a7d64c023a | refs/heads/master | 2021-06-07T06:51:20.640455 | 2021-04-22T02:32:58 | 2021-04-22T02:32:58 | 55,379,803 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 392 | r | bvi_plot.R | bvi_plot <- function(bvi_scores){
library(ggplot2)
library(dplyr)
library(tidyr)
taxunits <- colnames(bvi_scores)[1]
bvi_scores %>%
select(-c(BVI, rBVI)) %>%
gather(Sample, Score, -1) %>%
set_colnames(value = c("Spp", "Sample", "Score")) %>%
ggplot(aes(x = Sample, y = Score, fill = Spp... |
ae0787d33bcf9b47a7f59d53bc01e09a26897353 | 8c4a74b0a344440a15a2edee5bb761bcd2dfcad9 | /R/zzz.R | 4f41daa039ccab1ffcfdce80f9ea41fe91193e3c | [
"MIT"
] | permissive | xoopR/set6 | 341950b7649629dc9594b9230710df5140679bf7 | e65ffeea48d30d687482f6706d0cb43b16ba3919 | refs/heads/main | 2023-05-22T22:46:30.493943 | 2022-08-27T17:20:08 | 2022-08-27T17:20:08 | 197,164,551 | 9 | 0 | NOASSERTION | 2021-11-16T15:02:05 | 2019-07-16T09:36:22 | R | UTF-8 | R | false | false | 241 | r | zzz.R | #' @import ooplah
#' @importFrom R6 R6Class
NULL
# nocov start
.onLoad <- function(libname, pkgname) {
options(set6.unicode = l10n_info()$`UTF-8`)
}
.onUnload <- function(libname, pkgname) {
options(set6.unicode = NULL)
}
# nocov end
|
6f7bc34f8c99023e43fabb3d20caed0f2b01d39f | 889ba2e1d818e3f32a8e0febf17839f960841623 | /HW7/1.R | 37b34ceff3d482647ed559fab52c4a051aee40e8 | [] | no_license | WillMc93/EN.605.657 | f9b5a80599192eafa1ce98890fc87bef0b22eaa8 | febf01617025b99365e954dae42e2321c14f4a4f | refs/heads/master | 2022-11-05T17:43:43.650873 | 2022-10-27T20:13:56 | 2022-10-27T20:13:56 | 238,077,182 | 0 | 2 | null | null | null | null | UTF-8 | R | false | false | 1,711 | r | 1.R | library(HMM)
states <- c('M1', 'M2', 'M3', 'M4', 'M5', 'I1', 'I2', 'I3', 'I4')
symbols <- c('A', 'T', 'C', 'G')
t1 <- 0.9
t2 <- 0.1
t3 <- 0.4
t4 <- 0.6
t5 <- 0
t6 <- 0
transitions <- matrix(data=c(0, t1, 0, 0, 0, t2, 0, 0, 0,
0, 0, t1, 0, 0, 0, t2, 0, 0,
0, 0, 0, t1, 0, 0, 0, t2, 0,
0, 0, 0, ... |
d954829f4525c32d17196be29495501f5286442f | 0500ba15e741ce1c84bfd397f0f3b43af8cb5ffb | /cran/paws.compute/man/ec2_describe_network_insights_access_scope_analyses.Rd | 0a9ea8429cdd414687c95d12df8d325e50114d0c | [
"Apache-2.0"
] | permissive | paws-r/paws | 196d42a2b9aca0e551a51ea5e6f34daca739591b | a689da2aee079391e100060524f6b973130f4e40 | refs/heads/main | 2023-08-18T00:33:48.538539 | 2023-08-09T09:31:24 | 2023-08-09T09:31:24 | 154,419,943 | 293 | 45 | NOASSERTION | 2023-09-14T15:31:32 | 2018-10-24T01:28:47 | R | UTF-8 | R | false | true | 1,782 | rd | ec2_describe_network_insights_access_scope_analyses.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/ec2_operations.R
\name{ec2_describe_network_insights_access_scope_analyses}
\alias{ec2_describe_network_insights_access_scope_analyses}
\title{Describes the specified Network Access Scope analyses}
\usage{
ec2_describe_network_insights_access... |
53684accabfdd966abfafa0bdf470562a6697807 | 7d71864a94808408a08b1b73c7302987d4492f7e | /R/filter_variants.R | 612b02ebfab7002dbcdd788b59b914fe257deb96 | [] | no_license | komalsrathi/MendelianRNA-seq-analysis | ed77e3990efb59d32e3f2b756827ec26d605ce29 | 301b0e01bea6e0b9db810268c0331992e3385c4e | refs/heads/master | 2020-07-03T07:00:02.523330 | 2020-01-20T20:35:51 | 2020-01-20T20:35:51 | 201,830,611 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,048 | r | filter_variants.R | ####################################################################
# Author: Komal S Rathi
# Date: 01/31/2019
# Function: script to filter variants from GATK, Vardict and Strelka
# Mahdi's pipeline filters
# 1. ROI filter: exons +/- 10
# 2. Qual by depth: 5 for low GQ/low DP variants
# 3. Population Filters:
# HGMD... |
435071f7d57ee97139b3bdad7f3ed5a814ecbdd8 | d5c6d9895e35047d47718fec3decce9e728906b1 | /R/parse_taxolist.R | ff6703f72883dae200c964ec3c3312e870dbcc87 | [] | no_license | XingXiong/bioparser | db44cf1ec5e3e49cb73af3cd93b4bffd209f7b23 | 1f11a018563fe5c7ad737c4e3d01dcb0f220c5a7 | refs/heads/master | 2021-01-23T13:30:47.438492 | 2017-08-29T16:11:21 | 2017-08-29T16:11:21 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 26,318 | r | parse_taxolist.R | #' Parse and extract taxonomic names from txt files
#'
#' \code{parse_taxolist} reads and parses all text lines from a file which contains
#' taxonomic names, authors and distribution in each row and writes the tabular output
#' to a csv file automatically or based on the configuration \code {config} specified
#' by th... |
73eb3a888cafc52dca023fdedf5c3a0105355643 | 0a906cf8b1b7da2aea87de958e3662870df49727 | /grattan/inst/testfiles/IncomeTax/libFuzzer_IncomeTax/IncomeTax_valgrind_files/1610051626-test.R | 92c3715ddb78ae8d5931006601cca89f86938c65 | [] | no_license | akhikolla/updated-only-Issues | a85c887f0e1aae8a8dc358717d55b21678d04660 | 7d74489dfc7ddfec3955ae7891f15e920cad2e0c | refs/heads/master | 2023-04-13T08:22:15.699449 | 2021-04-21T16:25:35 | 2021-04-21T16:25:35 | 360,232,775 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,829 | r | 1610051626-test.R | testlist <- list(rates = c(NaN, 7.29112072938316e-304, -1.64816262214147e-307, -2.35343736497682e-185, 7036874417766.4, -2.35343736826454e-185, 7.17736025324585e-310, 7.32777351949015e-15, 9.14021444806306e-322, 1.00891829368495e-309, 2.67904643304077e+301, -1.26836459123889e-30, 9.37339630957792e-312, 1.0950979128... |
751186950e6b5da574d7a76c4373be228599161d | 6b01573263a626f7125dd3cba6f59c1c5024cc09 | /functions.R | 97dffcd62ed6266540cda65e5ec5865d6b7a1acb | [] | no_license | escanillans/eulerian_path_finding_algorithm | eb987b4bb45872d5eddb0cbea23b7a7fa6be65b6 | 617045c2d095060ae592f48ac2222d0b29530d06 | refs/heads/master | 2020-03-11T09:27:59.678758 | 2018-04-17T14:03:28 | 2018-04-17T14:03:28 | 129,912,126 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,980 | r | functions.R | # This function is the forward algorithm
# Input:
# n = number of states
# seq = sequence
# Output: log forward probabilities for corresponding sequence position and state
forward <- function(n, seq, transitions, emissions)
{
# create an empty matrix
# dimension = n+2 x length(seq)+1,
# where n = number of stat... |
bbe56a4000a400c15e953f99e80d70663d021204 | 50851ba9027ece2a6f0a23d493d5d15e4d3cc208 | /script/corr.R | c08e2b136146111ed0f07c6a8f574af63c18282f | [] | no_license | JonathanRyanW/R_Programming_Quiz2 | b6b8faca6600ebf71cb5586a6f9250eb6d0e63b2 | 3017a8079b8fc623f5735dcf02bc6e66abfae1df | refs/heads/main | 2023-03-05T12:31:17.324566 | 2021-02-24T00:10:23 | 2021-02-24T00:10:23 | 341,727,119 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,936 | r | corr.R | id_into_character <- function(id){ #id is an integer vector
id_char <- c() #creating an empty vector for id in character form
for (i in seq_along(id)){
if (id[i] < 10){ #if the integer has only 1 digit
id_char <- c(id_char, paste("00", as.character(id[i]), sep = ""))
}
else if (id[i] < 100){ #if t... |
857c45e446ed605298acdf51d1c2b0e332b9d377 | 32e0458f7a034d1bbc63b2e251ed485c8672fc53 | /man/pcc.Rd | fe1aa7a8b031a12091cda0b1e6aa142700695993 | [] | no_license | matthewwolak/nadiv | 8ac285b4d5d5e1de558b3de9019db1c81bdd6bce | 4d60f7c2a71149780c0cd33aee2b7735e8650619 | refs/heads/master | 2023-08-02T13:14:04.450579 | 2023-06-16T02:00:38 | 2023-06-16T02:00:38 | 33,896,065 | 16 | 7 | null | 2023-06-16T02:00:39 | 2015-04-13T21:52:53 | R | UTF-8 | R | false | true | 2,463 | rd | pcc.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/remlSupport.R
\name{pcc}
\alias{pcc}
\title{REML convergence checks}
\usage{
pcc(object, traces = NULL, tol = 0.01, silent = FALSE)
}
\arguments{
\item{object}{A list with at least one element named: \code{monitor} (see
Details)}
\item{trace... |
d114468fec6da1ad8fab1f5bc9b294312ffe3358 | 73ad0d1d6afe0beb099efd6817e2136326f20991 | /AutoCorrelation.R | 657dacce3698f51ee13533824975cca82ee6af27 | [] | no_license | prabasiva/cchaos | 7edce0367b53e685ff642dadee3b38adb140af5a | 07a399abaf559cb1bbecc9405b491a2e64c547ed | refs/heads/master | 2021-06-02T14:07:39.186551 | 2018-01-09T15:29:37 | 2018-01-09T15:29:37 | 67,543,385 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 7,052 | r | AutoCorrelation.R | ac<-function()
{
#Program used to create AutoCorrelation Analysis for sample, SP500 & NASDAQ
#Filename:AutoCorrelation.R
# Praba Siva
# praba@umich.edu
# @prabasiva
library(mFilter);
library(latex2exp)
setwd("/Users/sivasp1/Documents/2016/Personal/Praba/MATH599/program")
fspcom=read.table('fspcom.dat')
da... |
a62df1774fd2dfdf48bf9192dd0b472cb0831f51 | 2077b291538c221a6f2ceb36849d0d0630a5fb26 | /INF-0612/aula3/aula3.R | 1014c7ae30bbd8691ff554b24289bbb8c524ae15 | [] | no_license | arthurlustosa/MDC-Unicamp | 44ba0967019a22d375200cf070161a5f906c82fb | edaec371f16a7aaa661da6b87852d216ffb1c763 | refs/heads/master | 2021-02-27T21:06:20.458374 | 2020-03-07T14:04:52 | 2020-03-07T14:04:52 | 245,635,733 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 4,880 | r | aula3.R | # Definindo Funcoes
mysum <- function(a, b) {
a + b
}
makePower <- function(n) {
function(x) {
x^n
}
}
square <- makePower(2)
cube <- makePower(3)
subvector <- function(vector, begin = 1, end = length(vector)) {
return(vector[begin:end])
}
mydist <- function(x = c(0, 0), y = c(0, 0)) {
sqrt((x[1] - y... |
cf3822253d433ac548b68b7925b8c848af00b1cf | 4024b9299759390486555af4f8211e8544a2ac50 | /run.analysis.R | cec6f7364ef2f2e43efe4e379407922f62f98e74 | [] | no_license | JasonSklikas/Getting-and-cleaning-data-project | a888b126be741fb8246a50ab9e0d7b0df0567bc1 | fd2ceeecc8b5c244438c8ec3ae87f68d58bdfb79 | refs/heads/master | 2021-06-08T05:55:37.673805 | 2016-11-01T15:47:51 | 2016-11-01T15:47:51 | 72,547,090 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 2,622 | r | run.analysis.R |
#download and unzip the file.
if(!file.exists("./Project")){dir.create("./Project")}
url<-"https://d396qusza40orc.cloudfront.net/getdata%2Fprojectfiles%2FUCI%20HAR%20Dataset.zip"
download.file(url,destfile = "./Project/dataset.zip")
unzip("dataset.zip")
#1.Merges the training and the test sets to create one data... |
0ff96593f97023e332eb6b8c315d83cb00bb71ff | d7746351cdbf75c90e1fbcfbe337da50eb6dc1b1 | /man/disp2D-package.Rd | 45d323e191579dd4a1792f3911932371060ae75d | [] | no_license | cran/disp2D | 7295625024c1dd59936b33ef0bbc355994adcb61 | e2927c57f1a38ad405a07b165a7070e53773ca02 | refs/heads/master | 2021-01-23T15:03:27.066160 | 2012-05-24T00:00:00 | 2012-05-24T00:00:00 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,953 | rd | disp2D-package.Rd | \name{disp2D-package}
\alias{disp2D-package}
\alias{disp2D}
\docType{package}
\title{ Hausdorff and Simplex Dispersion orderings }
\description{
Given a 2D point set, different three point sets are selected.
The Hausdorff distances between the convex hulls are calculated
exactly.
}
\details{
\tabular{ll}{
Package... |
c2828e6faca563891665787e19741cbea4a467de | 52f95b07a1d460d90350d5dced856363d96b5aa0 | /Data Analysis 3 test file.R | e2f67ced77525e4e083634edf93cbd3a4d9214e8 | [] | no_license | twgg201/datan3_2019 | 16122d6f5a7271ba6932c677db97855f2a87e877 | 1a1c833eacce1cde029ad8aa504ae9d2983d1a39 | refs/heads/master | 2020-04-17T04:57:58.115699 | 2019-01-17T16:37:47 | 2019-01-17T16:37:47 | 166,255,456 | 0 | 0 | null | 2019-01-17T16:12:30 | 2019-01-17T16:12:30 | null | UTF-8 | R | false | false | 26 | r | Data Analysis 3 test file.R | #data analysis 3 test file |
84242d78e6522449036c65c40e66d589134c475c | 1be41b26870593e2c1320bfa7a5a8a207f7ef9ba | /dataset_splits/stratify.R | d3ec57712d9d49caf72b97db363a8b35c9a2d7ce | [] | no_license | NUNLP/AD_modeling | a133f1f4c53ec3c320bdbdbb0b53e4e9a84436de | 3323ce527aa8ec8a61d4a6e1dfd78df49ea9cadd | refs/heads/master | 2020-12-06T17:22:07.722750 | 2017-06-30T20:30:00 | 2017-06-30T20:30:00 | 95,596,854 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 1,715 | r | stratify.R | library(dplyr)
library(magrittr)
strat_sample <- function(data, gr_variab, tr_percent, seed) {
stopifnot(tr_percent > 0 & tr_percent < 1)
if(require(dplyr) & require(magrittr)) {
if (!missing(seed)) set.seed(seed)
names0 <- names(data)
gr_variab <- which(names0 == gr_variab)
... |
da41109b6dfb0c6f85b2ebf495b9faf84ee1ac08 | e40d274ff6b9bd7e7f20998379f483543582c81f | /apputils/man/update_toastr_css.Rd | 79ee44119690116ec68ef58557a33c84d82fdcaf | [
"MIT"
] | permissive | ua-snap/snap-r-tools | 5be2dcc5171cf7289504f20e98ad3ec603e4ed57 | c3f573c2abf11633b5262c4d98cfbde39854dbf4 | refs/heads/master | 2020-03-22T05:57:29.239067 | 2019-01-08T03:11:17 | 2019-01-08T03:11:17 | 139,602,296 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 2,151 | rd | update_toastr_css.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/apputils.R
\name{update_toastr_css}
\alias{update_toastr_css}
\title{Update shinytoastr css}
\usage{
update_toastr_css(container = NULL, toast = NULL, rgba = NULL,
hover.rgba = NULL, opacity = NULL, hover.opacity = NULL,
radius = NULL, po... |
61df754e3cdb802482e174335607cce9fcaa8b79 | ab0ec24c7b111c5b333cc5b1f459f56db7adb5d6 | /plot2.R | 3ad9ac68f8b8f8ecacbcfaaba2e55cd9b1a289e4 | [] | no_license | RichardSobota/ExData_Plotting1 | d8a2572c88c998a18f00bfd47fa4cb9c36b381de | 181cf87f869b920f3417fdd31b0b4caceb3d7afd | refs/heads/master | 2021-01-22T11:03:23.080679 | 2015-02-08T19:30:12 | 2015-02-08T19:30:12 | 28,894,790 | 0 | 0 | null | 2015-01-07T02:41:43 | 2015-01-07T02:41:42 | null | UTF-8 | R | false | false | 1,213 | r | plot2.R | ##
## plot2.R
##
## Written by Richard Sobota as part of programming assignment
## in Exploratory Data Analysis course.
##
## Function uses packages dplyr and lubridate.
##
plot2 <- function() {
## read data from file
csv.data <- read.csv("household_power_consumption.txt",
sep=";",
... |
5f55a8b763474c93d54a0372e128f119470e5030 | f15700323e86bd4cc22886fe0329757c9bf7fc34 | /man/lre_auto_bk.Rd | 88eca49a9ee4f2b6c12317efce9f61c871c7e711 | [] | no_license | kenjisato/lrem | 699fac35a3f6d5d77801dd77d62ddc1608389d50 | 33068384970231d169704108866c1b9de369059c | refs/heads/master | 2020-05-07T12:19:28.330011 | 2017-06-12T04:16:32 | 2017-06-12T04:16:32 | 180,499,821 | 0 | 0 | null | null | null | null | UTF-8 | R | false | true | 709 | rd | lre_auto_bk.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/algorithm-bk.R
\name{lre_auto_bk}
\alias{lre_auto_bk}
\title{LRE solution method based on Blanchard and Kahn (1980, ECTA)}
\usage{
lre_auto_bk(A, nx)
}
\arguments{
\item{A}{Square matrix}
\item{nx}{The number of predetermined variables, \cod... |
733b24a9f044ac090d9c3f9cd63449fcf81c930e | 0ac12d92c092548517fd4c3f8bcb2849567b290a | /man/create_seurat_obj.Rd | e4f99e16efccb5fc3c8eaec0e02d803fbd4be524 | [
"MIT"
] | permissive | igordot/scooter | 9422ea52ab634870ddac1bb4ce435f10057f0207 | cff289d15e8b2ac7704ade6e1c962e7bf0c1f674 | refs/heads/master | 2023-02-09T14:04:13.117319 | 2023-01-23T23:19:20 | 2023-01-23T23:19:20 | 145,920,527 | 5 | 3 | null | null | null | null | UTF-8 | R | false | true | 776 | rd | create_seurat_obj.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/import.R
\name{create_seurat_obj}
\alias{create_seurat_obj}
\title{Create a new Seurat object from a matrix.}
\usage{
create_seurat_obj(
counts_matrix,
assay = "RNA",
min_cells = 1,
min_genes = 1,
log_file = NULL,
project = "proj"... |
b988a51671653364a8c3cc38ec448e91af5b98b5 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/GpGp/examples/order_dist_to_point.Rd.R | c7dc2255df385823aafde6d89382eac7fd2401f9 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 326 | r | order_dist_to_point.Rd.R | library(GpGp)
### Name: order_dist_to_point
### Title: Distance to specified point ordering
### Aliases: order_dist_to_point
### ** Examples
n <- 100 # Number of locations
d <- 2 # dimension of domain
locs <- matrix( runif(n*d), n, d )
loc0 <- c(1/2,1/2)
ord <- order_dist_to_point(locs,loc... |
3b6676b7f66c949a58bf8607fd79d9a0e14f6389 | 447b1e30413599ff5306408b9383937f5c1bef36 | /R/discrete-gamma-distribution.R | b6875d398e7ad535ef1411aa151b5ff05c2be4c1 | [] | no_license | twolodzko/extraDistr | 874768df1d0c1af75924be8f2cc872e222c3bb6d | 6cdbe85a98c3a34d8360b8c0ffe6eb78517e0fc3 | refs/heads/master | 2022-11-11T03:35:25.808964 | 2022-11-08T10:41:21 | 2022-11-08T10:41:21 | 55,365,786 | 41 | 10 | null | 2022-06-25T20:24:44 | 2016-04-03T19:51:19 | C++ | UTF-8 | R | false | false | 2,464 | r | discrete-gamma-distribution.R |
#' Discrete gamma distribution
#'
#' Probability mass function, distribution function and random generation
#' for discrete gamma distribution.
#'
#' @param x,q vector of quantiles.
#' @param n number of observations. If \code{length(n) > 1},
#' the length is taken to... |
aa0555c5076e3876a58bd6363bc965bf0ae7facb | 53010da0027d6b6b7a44309d36e26010e0852681 | /dvnorm_paper_v2.r | 0182cfa80845dcc5730b419ad73483b631caf9b2 | [] | no_license | jhhughes256/LEN_PK | 7575ea8e92ecf2638de2ffdf641b9c1b052c7280 | 6f19454fc314728c536e94ccd762748d603132e2 | refs/heads/master | 2022-01-07T14:43:38.819059 | 2019-06-14T02:22:56 | 2019-06-14T02:22:56 | 62,599,627 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,919 | r | dvnorm_paper_v2.r | ###nmprep.r
##Goal: To collate tables of missing data contained within nonclinical raw data obtained on 23rd March 2016
##Note: Based heavily off of datacheck_cyt_script2.r -> Richards code
# Remove any previous objects in the workspace
rm(list=ls(all=TRUE))
graphics.off()
# Set the working directory
master.dir... |
71fa075d9caea77d286c9b1f10bbe387c2fbe723 | 8baf888790f25fb9cdfb45cbda64cc9bba38c2a2 | /obs_fixes.R | 315ba2f9585b0ecbec98f25ed9d1feb90ba0f023 | [] | no_license | NIVA-Denmark/NISAR_app | 6a79aa489dc0047f7aad2883b2779c77eac47952 | b4aed56e3ba1a48e94725a9f0f70c3f99cf207cc | refs/heads/master | 2021-07-25T15:16:35.562414 | 2020-12-11T13:43:25 | 2020-12-11T13:43:25 | 228,263,375 | 0 | 0 | null | 2020-12-11T13:43:26 | 2019-12-15T22:41:23 | R | UTF-8 | R | false | false | 1,295 | r | obs_fixes.R | if(F){
dfObs <- read.table("data/NISAR_obs.csv",stringsAsFactors=F,header=T,fileEncoding="UTF-8",sep=";")
dfObs <- dfObs %>%
filter(!ShapeID %in% c(168,169))
dfObs <- dfObs %>%
filter(AphiaID != 127188)
dfObs <- dfObs %>%
filter(Source != "KU Fish")
write.table(dfObs,file="data/NISAR_obs.csv",col.names=T,ro... |
817ccadc8eaf7df5ad6c29d6a523a52eb09ffd94 | be8c9660ff29a44d1835b74b3ec861cd76adb834 | /results/fig3_gsebias-sim-plots-tables.R | e6ad7446086c1bfd7bc62050d28fd15943d0bc37 | [] | no_license | metamaden/recountmethylation_flexible-blood-analysis_manuscript | ec9ba3666db953430ec1be509a826d45fba97f57 | ec835f346da6bcb628ac262d22c5827936610981 | refs/heads/main | 2023-04-16T20:18:31.234484 | 2023-02-02T20:33:38 | 2023-02-02T20:33:38 | 401,501,606 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 12,219 | r | fig3_gsebias-sim-plots-tables.R | #!/usr/bin/env R
# Author: Sean Maden
#
# Make heatmaps of GSE bias simulations results.
library(ggplot2); library(data.table)
library(scales); library(gridExtra)
library(ggpubr)
# library(magick)
#----------
# load data
#----------
# load data tables
# sum of squared variances table
msq.fname <- "msq-gse-bias_all-b... |
d99949f033fc334728e8bac8e7bb83cfb1482b96 | ed640b2eab34ddbde1435b83aa29d49d2c01422d | /man/vda.Rd | b30981c711d5c8308adbfc854b0caa5d1df62d2e | [] | no_license | cran/rcompanion | 4cf285cf6d43197e55df85de86d23904f9418c37 | dea4b790b5d78fe350ff303e5c04603c7e672ae1 | refs/heads/master | 2023-05-12T14:48:28.937161 | 2023-05-05T07:20:05 | 2023-05-05T07:20:05 | 67,362,460 | 3 | 2 | null | null | null | null | UTF-8 | R | false | true | 4,059 | rd | vda.Rd | % Generated by roxygen2: do not edit by hand
% Please edit documentation in R/vda.r
\name{vda}
\alias{vda}
\title{Vargha and Delaney's A}
\usage{
vda(
formula = NULL,
data = NULL,
x = NULL,
y = NULL,
ci = FALSE,
conf = 0.95,
type = "perc",
R = 1000,
histogram = FALSE,
reportIncomplete = FALSE,
bru... |
d5aa6a34d882ea9cc42f2637cef2f633cd2785cf | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/kmconfband/examples/noe.compute.cgh.Rd.R | 1b5670746c2ce0444f6cd4e9b9901ed3885a2698 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 406 | r | noe.compute.cgh.Rd.R | library(kmconfband)
### Name: noe.compute.cgh
### Title: Intermediate Steps in the Noe Recursions for the Exact Coverage
### Probability of a Nonparametric Confidence Band for the Survivor
### Function
### Aliases: noe.compute.cgh
### ** Examples
## Check of Noe recursion calculations.
a<-c(0.001340,0.028958,0... |
988ea5e6b48fbba3aaf1af388d91be9a69a0edd4 | 79f67b255bf060cbe2847541df4aff09d4c339e9 | /no1.R | 4c423c619645206b283fd6edba507aec19bb23d3 | [] | no_license | mspub/R_TEST | 6c7fabe10016c4984e6c2b8397dd1b2480a23285 | 28f8a2dd6cc8373607744905f4fc9b429e341f7b | refs/heads/master | 2021-01-19T00:01:36.674840 | 2016-07-13T04:46:29 | 2016-07-13T04:46:29 | 63,214,057 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 471 | r | no1.R | ?install.packages
old.packages()
head(iris,n=10)
summary(iris)
var(iris$Sepal.Length)
(v<-c(1,3,4,6))
a<-v[c(1,2,3)]
a
v
v[v>2]
v>2
v[-1]
v[-3]
v[-lenghth(v)]
v[-length(v)]
data=read.table(header=T, text='
subject sex size
1 M 7
2 F 6
3 M 11
... |
608d69d6212c47a481bf6c193c524a0d55c0ebcb | 7eb2898925eb5b0f04729cc8c151cd751277569a | /R/query.r | 92c389ccb2f027d531811dfbe474400aa765cdc4 | [
"MIT"
] | permissive | hrbrmstr/sergeant | d23a7443136d921a9a5fdf4d8c219076afa55af5 | 9408208e41ecb2636d4a9856d32789ffb93b9d96 | refs/heads/master | 2022-05-06T11:56:21.690396 | 2021-11-29T18:06:59 | 2021-11-29T18:06:59 | 60,310,735 | 137 | 20 | NOASSERTION | 2021-11-29T18:07:00 | 2016-06-03T02:03:16 | R | UTF-8 | R | false | false | 7,088 | r | query.r | #' Submit a query and return results
#'
#' This function can handle REST API connections or JDBC connections. There is a benefit to
#' calling this function for JDBC connections vs a straight call to \code{dbGetQuery()} in
#' that the function result is a `tbl_df` vs a plain \code{data.frame} so you get better
#' defau... |
edd91a6f6676957e9e0575fc6d4b637cb7a8b246 | f7ec59a2df6950794de998015e3b6372865e34df | /A549/scripts/chris/NB_balance/analysis_GainLossNB_genes.R | f693df9290e0d3673d979ae491a384f01ad6c469 | [] | no_license | ArnaudDroitLab/sb_cofactor | e89e0ec8ce54033be723ec34d2455834dba9bf2d | 77e5c922ecae7b4c66f140f5525085ab03344ec1 | refs/heads/master | 2021-01-23T05:29:40.591863 | 2019-09-27T17:30:41 | 2019-09-27T17:30:41 | 92,969,805 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 3,844 | r | analysis_GainLossNB_genes.R | # setwd("/Users/chris/Desktop/sb_cofactor_hr/A549")
setwd("/home/chris/Bureau/sb_cofactor_hr/A549")
source("scripts/ckn_utils.R")
library(ChIPseeker)
# Loading peaks
peaks_dir <- "output/chip-pipeline-GRCh38/peak_call/A549_NB"
gainNB_ovGR <- rtracklayer::import(con = file.path(peaks_dir, "NB_DEX_to_None_CTRL_ovGR_hg3... |
9ce85901e01b6cbd3d57b7ff69769b237d4aa5e8 | b6ca93afe5eecaf5bb8a5f2989095da324c58b8c | /RealData/Code/JSMultistateInfFunctions.R | 9e669c4ec2d13dacc2186aca4837f99a4090f5f7 | [] | no_license | angieluis/BayesianMarkRecapSNV | 51a6428770cb39202c090b4a287008f00ab74916 | b67a59194b3282cd41f3b13946a7437eecc46eb8 | refs/heads/master | 2023-02-16T15:10:48.702511 | 2023-02-10T20:16:33 | 2023-02-10T20:16:33 | 119,726,664 | 7 | 2 | null | 2018-09-28T20:17:12 | 2018-01-31T18:31:42 | R | UTF-8 | R | false | false | 3,318 | r | JSMultistateInfFunctions.R | logit=function(x){
log(x/(1-x))}
revlogit=function(x){
exp(x)/(1+exp(x))}
# function to create a primary CH from the secondary capture history:
primaryMS.fun<-function(CH.secondary){
x <- lapply(CH.secondary,function(x){apply(x,1,min)})
v1 <- unlist(x)
CH.primary <- matrix(v1, nrow=dim(CH.secondary[[1]])[1]... |
e16472a9bfea902a19a72ac8c23e870913b46586 | ffdea92d4315e4363dd4ae673a1a6adf82a761b5 | /data/genthat_extracted_code/RCircos/examples/RCircos.Get.Heatmap.Color.Scale.Rd.R | f2bf6583193ba3ffdc51a52d05c5e3977d5f8882 | [] | no_license | surayaaramli/typeRrh | d257ac8905c49123f4ccd4e377ee3dfc84d1636c | 66e6996f31961bc8b9aafe1a6a6098327b66bf71 | refs/heads/master | 2023-05-05T04:05:31.617869 | 2019-04-25T22:10:06 | 2019-04-25T22:10:06 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 293 | r | RCircos.Get.Heatmap.Color.Scale.Rd.R | library(RCircos)
### Name: RCircos.Get.Heatmap.Color.Scale
### Title: Generate Color Scales for Heatmap Plot
### Aliases: RCircos.Get.Heatmap.Color.Scale
### Keywords: methods
### ** Examples
library(RCircos)
colorScales <- RCircos.Get.Heatmap.Color.Scale(heatmap.color="BlueWhiteRed")
|
ed2660b4499a70323fa663fd635fb6c47b58b5e2 | 7209adb2c925cce4dcdb83499f92485790ddb84d | /oldScripts/aylinsMonster/Franken_SPACE_5-4-15.R | 992330259dd367b324292553a10c92bf814164e9 | [] | no_license | TinasheMTapera/Reward | e45942891261fa28bf79d2d13b4f87fbbc6736af | ed3e7a5c10a06a0a4a8dd4d6ac0ab1aefce3bb25 | refs/heads/master | 2023-08-19T20:23:37.374939 | 2021-06-22T17:45:03 | 2021-06-22T17:45:03 | null | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 9,130 | r | Franken_SPACE_5-4-15.R | ### converts NSRS, FRANKEN & FRANKEN ADOL. item-level data from giant redcap project (Wolf Satterthwaite Repository) into New Franken Space ###
# updated on 2/1/16 to run from Selkie server instead of Banshee
# records must be updated on selkie from banshee for active projects (like effort) before it is run otherwise t... |
25b4ebf9b83aa20bf62ac50a2384e119a9385831 | cafff9b400a5f31e92176ec294517cdc43a8dc86 | /Zero models.R | 70d2b420693903d973f532703b97306d9fddb366 | [] | no_license | camillemellin/TrueAbsencesInSDMs | df279c65efc6d5da41130e7180920154d9a79018 | 7af2357c28b028ef0fdc7870d6e698aee43069b8 | refs/heads/main | 2023-01-23T08:59:35.589144 | 2020-11-30T03:48:40 | 2020-11-30T03:48:40 | 304,510,467 | 1 | 0 | null | null | null | null | UTF-8 | R | false | false | 39,343 | r | Zero models.R | ##################################################################
# ZERO MODELS - CM 09/07/19 #
##################################################################
# Load libraries ------------
rm(list = ls())
library(dplyr)
library(stringr)
library(RLSPrivate)
library(psych)
libr... |
f7c9467ec0028c9c54b7ad886d397580f20d325f | 7a95abd73d1ab9826e7f2bd7762f31c98bd0274f | /meteor/inst/testfiles/ET0_ThornthwaiteWilmott/AFL_ET0_ThornthwaiteWilmott/ET0_ThornthwaiteWilmott_valgrind_files/1615831663-test.R | 4905af3f68601581bf6b8ceae855275f7045eb73 | [] | no_license | akhikolla/updatedatatype-list3 | 536d4e126d14ffb84bb655b8551ed5bc9b16d2c5 | d1505cabc5bea8badb599bf1ed44efad5306636c | refs/heads/master | 2023-03-25T09:44:15.112369 | 2021-03-20T15:57:10 | 2021-03-20T15:57:10 | 349,770,001 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 683 | r | 1615831663-test.R | testlist <- list(doy = c(0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0), latitude = numeric(0), temp = c(8.5728629954997e-312, 1.5688525430436e+82, 8.96970809549085e-158, -1.3258495253834e-113, 2.79620616433656e-119, -6.80033518839696e+41, 2.68298522855314e-211, 1444042902784.06, 6.68889884134... |
48da30463329abf37009844f11b06501f44b0985 | 8a97255cb66455dbef0cf01864a3b334cf20a66b | /MMModellerEngine/ModellerEngineFunctions.R | cc4fecf71736c4ffc7bdb8d9bbc15aabddc32f79 | [] | no_license | AshutoshAgrahari/R_Practice | c56bbb3c0893e101305f150c0b74045f24cf5a44 | 4c31ce94f130b363f894177a1505ccac290547e0 | refs/heads/master | 2020-03-19T17:51:05.826260 | 2020-01-25T10:34:55 | 2020-01-25T10:34:55 | 136,781,266 | 0 | 0 | null | null | null | null | UTF-8 | R | false | false | 216,653 | r | ModellerEngineFunctions.R | #############################################################################
####################### ModellerEngien Licensecing & Login ##################
#############################################################################
# sidebar Modelling Technique functions
Am_function <- function(){
menuItem("AutoMo... |
f828d26d8b1de57632621d53d509c7cc8f4ac2e6 | 6a28ba69be875841ddc9e71ca6af5956110efcb2 | /Introduction_To_Probability_And_Statistics_For_Engineers_And_Scientists_by_Sheldon_M._Ross/CH5/EX5.8.e/Ex5_8e.R | af07ae4112b18cd7159a03bcce14535817472eda | [] | permissive | FOSSEE/R_TBC_Uploads | 1ea929010b46babb1842b3efe0ed34be0deea3c0 | 8ab94daf80307aee399c246682cb79ccf6e9c282 | refs/heads/master | 2023-04-15T04:36:13.331525 | 2023-03-15T18:39:42 | 2023-03-15T18:39:42 | 212,745,783 | 0 | 3 | MIT | 2019-10-04T06:57:33 | 2019-10-04T05:57:19 | null | UTF-8 | R | false | false | 32 | r | Ex5_8e.R | #a
pt(1.4,12)
#b
-qt(0.025,9) |
5adc1ab4ecd53b19ad3138888f5f36e6f732b3cf | 164f95bc07a0655806d4169e67e8e4aeb61bfbb7 | /tools/music_deconvolution/scripts/estimateprops.R | 92e57337aa391744a91ac6a0330e8d7c05feac91 | [] | no_license | Delphine-L/galaxytools | 4ce7cf82e9302820b84b7f2a50530174d3f02f13 | 479bcc60983d5957ec5609dc6a58d8e927ce4af6 | refs/heads/master | 2021-11-23T22:18:16.046656 | 2021-11-09T08:36:48 | 2021-11-09T08:36:48 | 250,547,957 | 0 | 0 | null | 2020-03-27T13:56:05 | 2020-03-27T13:56:05 | null | UTF-8 | R | false | false | 4,074 | r | estimateprops.R | suppressWarnings(suppressPackageStartupMessages(library(xbioc)))
suppressWarnings(suppressPackageStartupMessages(library(MuSiC)))
suppressWarnings(suppressPackageStartupMessages(library(reshape2)))
suppressWarnings(suppressPackageStartupMessages(library(cowplot)))
## We use this script to estimate the effectiveness of ... |
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